Cephalometric analysis is commonly used by dentists and orthodontist to study skeletal relationships in the craniofacial complex. Cephalometric analyses can be used to evaluate a patient's dentofacial proportions, and help doctors recognize abnormalities, predict future changes, and study the success of ongoing treatment plans.
A Cephalometric radiograph is an x-ray radiograph of the head taken in a Cephalometer (Cephalostat) that is a head-holding device introduced in 1931 by Birdsall Holly Broadbent Sr. in USA and by H. Hofrath in Germany. The Cephalometer is used to obtain standardized and comparable craniofacial images on radiographic films. A conventional cephalometric analysis involves the acquisition and initial cephalometric radiograph examination, as well as post-examination reading of the radiograph or related information.
FIG. 1 shows a sequence of frames 100 representative of common steps in current cephalometric procedures according to the prior art, which steps will be discussed in more detail below. Frame A shows a representative cephalometric radiograph as acquired by a modern cephalometer machine. Frame B shows major features of interest in the cephalographic process, including some groups of data found to hold clinical significance by those skilled in the art. Frame C shows individual points of interest related to said major features of interest, overlaid on the cephalographic radiograph of Frame A. Frame D shows a graphical representation of various geometric relationships derived from the positions of the points of interest of Frame C, which can be used to characterize clinical conditions of a patient and which can be used to guide future treatments of the patient.
FIG. 1A shows the cephalometric radiograph 102 of Frame A of FIG. 1, which may be generated from a plurality of base x-ray images to result in the final representation 102. For example, a plurality of individual base x-ray images may be gathered by movement of an x-ray imaging apparatus with respect to the patient (or vice versa) so as to acquire multiple of views of the patient's anatomy, e.g., sweeping the x-ray imager through a lateral 180-degree arc about the stationary patient's head. The plurality of views may then be processed using computer software to yield the shown representative two-dimensional (2D) representation 101, which is stored in a computer memory device as a gray scale cephalometric image, or in some other suitable format. These images may then be examined by human experts, technicians, transmitted for remote analysis, or for computerized post-processing.
Some cephalometric features have been recognized as clinically relevant in the art, shown in FIG. 1B. Examples of such cephalometric landmarks are the sella (marked ‘S’) and the nasion (marked ‘N’). A prime symbol (′) usually indicates the point on the skin's surface that corresponds to a given bony landmark (for example, nasion (N) versus skin nasion (N′). FIG. 1B shows the location of some typically required features or groups of landmarks in cephalometric analyses.
FIG. 1C shows individual landmarks (points) of interest as identified on a given patient's cephalometric radiograph, e.g., FIG. 1A. Traditionally, in order to perform the post-examination reading for a patient, dozens of anatomical landmarks representing certain hard or soft tissue anatomical structures are found on the radiograph by a trained human expert. In some examples about ninety points of interest or landmarks are identified and used in the analysis. For example, a technician views the radiograph on a computer screen, and with the aid of a computer mouse, track ball, sensor pad or similar device, annotates various points of interest (landmarks) on the image, e.g., by visually manipulating a cursor position on the screen and selecting the points of interest using a computer mouse button, key, or similar input device. Computer software then marks and stores the indicated points of interest with respect to image 101, which can be saved in a file for further processing, viewing or analysis.
Cephalometric landmarks are used as reference points for the construction of various cephalometric lines or planes and for subsequent numerical determination of cephalometric analysis measurements. FIG. 1D shows an exemplary result 108 of geometrical relationships among the identified landmark points of interest for a patient following acquisition and landmark identification on the patient. The relationships and geometric angles, distances and so on can be quantified to make clinical diagnoses or treatment plans for the patient.
For example, as shown, landmark points can be joined by lines to form axes, vectors, angles, and planes. For example, the sella and the nasion together form the sella-nasion line (SN or S-N).
The resulting cephalometric tracings outline the particular measurements, landmarks, and angles that medical professionals need for treatment. By using a comparative set of angles and distances, measurements can be related to one another and to normative values to determine variations in a patient's facial structure.
One example of a result typically generated in cephalometric analysis is a Jarabak analysis, developed by Joseph Jarabak in 1972. The analysis interprets how craniofacial growth may affect the pre and post treatment dentition. The analysis is based on 5 points: Nasion (N), Sella (S), Menton (Me), Go (Gonion) and Articulare (Ar). They together make a Polygon on a face when connected with lines. These points are used to study the anterior/posterior facial height relationships and predict the growth pattern in the lower half of the face. FIG. 1D shows an example of a Jarabak tracing-part of a Jarabak analysis used by medical professionals.
While the initial x-ray can be completed within minutes, the post X-ray analysis can take much longer. Once the landmarks have been determined by an expert, the various cephalometric analyses are commonly generated electronically used as a way to save time and reduce errors.
However, full automation of the initial manual step of determining the location of landmarks on the x-ray image is challenging due to overlaying structures and inhomogeneous intensity values in radiographic images, as well as anatomical differences across subjects.
Recently, a method has been proposed for automatically annotating objects in radiographic images by using predictive modeling approaches based on decision trees [T. Cootes, C. Lindner, M. Ionita; USPTO publication US2015/0186748 A1]. With this method cephalometric landmarks have been detected [C. Lindner and T. F. Cootes, ISBI 2015] using Random Forests regression-voting in the Constrained Local Model framework (RFRV-CLM).
A random forest is an ensemble of decision trees that outputs a prediction value. A decision tree is a flowchart-like structure in which each internal node represents a “test” on an attribute, and each branch represents the outcome of the test. A decision tree maps observations about an item to conclusions about the item's target value, in this case the spatial location of one particular landmark. Each decision tree is constructed by using a random subset of the training data, creating a ‘forest’ of trees. The final prediction of location of a particular landmark is created by combining the decisions of all trees in some way, for example by averaging.
Random forest-type techniques may not provide the accuracy needed in certain applications. For example, the random forest method requires a programmer to define the features being sought ahead of time, and does not permit for automated self-learning in a machine. Therefore, for applications where many (e.g., dozens or hundreds) of features are under investigation, this can be impossible to program and define by a programmer in an acceptable manner, especially given the likely variations in a patient's anatomy and image quality. In addition, random forest methods are best suited for one dimensional array inputs, and as such are at a disadvantage in handling two dimensional cephalometric and similar imagery. Existing random forest based applications therefore can suffer in accuracy and reliability compared to other methods, or even compared to human analysis.
The currently-used step of identifying the required landmarks by hand by a human operator is time-consuming, and the results are inconsistent and subjective. For example, the level of training, skill and experience of operators tasked with locating the landmarks of interest, in FIG. 1C above, can vary. An operator may also provide variable results based on his or her degree of interest in the work, fatigue, eye sight, hand coordination, and other factors. The results could also vary if an operator is very busy, rushed, or under economic or other pressure to finish the task at hand. Even a same operator, working on the same image, and operating with his or her best level of care, would likely generate somewhat different results from attempt to attempt, as the operator's ability to identify an exact pixel on the image under review will not be fully reproducible from time to time. Failures, errors and inaccuracies, or inconsistencies in landmark feature identification will translate directly to errors and inaccuracies in the results of the analysis of the landmarks, and may contribute to liability by the practitioner.
In addition, human operators are relatively inefficient and slow to take in, understand, and annotate the dozens of landmarks of interest on cephalometric radiograph images. Wages paid to well-trained quality technicians who analyze cephalometric images are an economic factor that increases health care costs and reduces profits to practitioners. A tradeoff between haste and accuracy, still limited by the speed at which the human operator works, limit the economic gains obtainable in current cephalometric image analysis systems and methods.
Therefore, there remains a need for an improved landmark identification process that can effectively replace or augment the manual process of choosing a multitude of standardized anatomical points on a cephalometric x-ray. This disclosure addresses a method for doing so, and a system and method for providing cephalometric analyses based on automatic landmarking in a cost-effective, accurate and consistent fashion.